At times, a user will want to search for a product by color even though it is an attribute that cannot be described adequately using words. For example, other than using rudimentary color names, such as “red” and “blue,” searching for products of a particular shade using color as a parameter is extremely difficult, even when the color is relatively popular and intuitively should be easy to locate. For example, there are numerous colors which would fit the simple “red” or “blue” description, and searching using the textual word “red” is not likely to bring up the specific red or the specific product of interest. Also, searches based on a particular type of color by name, such as “rose red” or “ocean blue” are unlikely to turn up the color of interest, as there may be a number of different colors, each with a different name or with multiple names varying by the naming convention used. Similarly, searching for a pattern made of colors, such as “blue and red stripes” is unlikely to turn up the desired pattern of particular colors.
Many of the drawbacks involving color-based searching stem from the nature of internet searching, which has historically been text-based, thus requiring a user to enter text into a search engine to describe the information sought. With regard to color, textual color names are typically tagged or embedded beneath an image of a product or associated webpage as metadata, making it virtually impossible to obtain reliable and complete search results when specific color shades are sought. More specifically, because many search systems that implement searching based on a color (or a pattern) are operable only as text searching, a system may allow a user to select a color by name or even “click” on the color (in the form of a color swatch) and then search for the selected color. However, in these instances, the system typically converts the inputted search parameter to a text-string associated with or representing a particular color. For example, a search system may search based on clicking red swatch on a webpage but converts the click to a search for “red” as text, but not as an actual color.
In such a system, the name of the color “red” is “tagged” to an image by way of a text string and the search is based by matching the input “red” to the text string “red” on the tag, and not to the color. From a consumer's perspective, such a system is insufficient to reliably capture all relevant products of a particular shade of red that are being sought. From a merchant perspective, such a system does not allow for dynamic analysis or codification of color that is a crucial but missing data set in understanding consumer preferences.
Another problem with contemporary color searching is a lack of universal color codification and unifying color-naming conventions. For example, even when a search using a specific color such as “cherry red” yields some relevant results when utilizing a search engine or a search field on a particular merchant's website (i.e., where the merchant utilizes the term “cherry red” as a tag to identify some of its products), such searches do not yield all of the relevant results for the particular type of red being searched. This is the case even when there are available products sold by other merchants that have the identical color or a close equivalent color but which use a term other than “cherry red” to identify that color.
Even color systems that offer naming conventions suffer from underlying drawbacks in their inconsistent application by merchant users and their vendors. For example, a wholesale buyer for a retailer may decide to order a line of products from a vendor in a color that is identified as “cobalt blue.” A second wholesale buyer at the same retailer may order another line of products from a second vendor in a color that the second buyer also identifies “cobalt blue,” having the intention that the colors be precisely the same so that a purchaser of product from the first line will be more inclined to purchase the second line of product as a matching set. Indeed, the variation in color between two products that purportedly have the ‘same color’ can be remarkable when the products are placed side by side. The lack of consistency among vendors and suppliers, even when the same color names are utilized, is often not appreciated until after the products arrive, at which time it is too late to ameliorate the situation.
Direct searching based on a particular color or a swatch has not been effectively accomplished. For example, if a user is in possession of one article of clothing and wishes to purchase a matching item, existing tools leave the user with the burden of determining the color of the clothing and what a matching color might be. Thus, the user is left to matching based on what “appears” to match (subject to variations in color on a screen).
Also, it has been left to the user to make the match and has not been done automatically. For example, it would be beneficial to make color matches that are similar to colors chosen by friends or members of affinity groups. Automatically making color selections for a user based on the aggregate demographic groups in which the user is a member has been nearly impossible.
Current systems further lack the ability to aggregate a user's preferred and/or customized colors onto a unified area or palette for purposes of identifying and searching for products. Individuals typically have preferred colors. and it would be beneficial to have that group of preferred colors collected and readily available to that user in a single palette. Also, use of the palette for forming color combinations and to perform searches based on a primary color and a secondary color (and a pattern) are lacking in the prior art. To that end, it would be beneficial to have that group of preferred colors identified, collected and readily available to that user in a single palette for effective color-based searching.
Another deficiency of prior art systems is the inability for a user to share and communicate his or her color preferences in order to facilitate the purchase of a particular product in a particular color or pattern. Because of this shortcoming, there is a further inability for a user to readily share any color preference with a friend, colleague, or other acquaintance with whom a user may be associated, such as a social networking affinity group. Such sharing capabilities would facilitate a member of a user's affinity group's purchase of an item for the user. For example, in the instance where a user discovers a desired product, say for a wedding or baby registry, that user may wish to save and share that information, particularly the color information, with friends and family. With the rise of computer technologies, connecting and sharing personal information with friends or other networks has become easier and more accessible. In connection with searching and selecting certain items or products, a number of tools and systems are designed for creating and sharing a registry of desired items or products. Nonetheless, these registries are based solely on items and products selected by the registrant and not on products that may be desired by a registrant based on, for example, color preference, demographic information, or color trending information. This information, such as color preference, sizing information, previous purchase information, and the like, may all be utilized by a user to make in-store purchases using a personal shopping assistant application that incorporates all of the user's data, and the data of those in the user's affinity groups. In this manner, the user is able to make a more informed purchase.